Uniform Manifold Approximation and Projection (UMAP)

- Minor performance accelerations.
- Code cleanup.
-run_umap insists on a MATLAB version before 2025 if the verbose argument is ‘graphic’. R2025 removes Java access to MATLAB figures/windows.

Minor bug fixes, feature improvements, and code accelerations based on two years of writing bioinformatics papers.

Fixes and improvements based on feedback from CYTO 2023 conference.
Testing with R2023a release

Corrected documentation in run_umap for examples 4 & 5 which use FlowJo.

1. Integration with FlowJO
- Import data and supervision labels from workspaces
- Export results to workspaces
2. Multidimensional scaling views supervised template reduction.
3. HeatMap improvements.
4. Many bug fixes and other improvements

1) Improved documentation and examples for using MLP train/predict independently of UMAP
2) MlpPython.Predict function
-Is faster on r2019b or later
-Allows test set with all OR MORE of the training set columns in any order

-mlp_train combines neural network and supervised template classification
-job_folder allows batching runs of run_umap from external software without MATLAB reloads
Example 34 in run_umap.m illustrates these new arguments and others

1. Fast approximation now accelerates both matching and reduction processing.

2. Prediction table now:
a) Displays dimensions for true+, false+ and false- stacked together.
b) Highlights selections as yellow on UMAP and EPP plots.

V3.0 improves speed, classification assessment and ROI functionality. For details see the last section of the FileExchange description and/or search the run_umap.m file for fast_approximation, run_epp and match_predictions.

-New table showing density distribution & KLD of unreduced data associated with groupings of the reduced data
-New run_umap arguments for supervised templates and accessing prior UMAP features
-New examples with larger data sets

Fix edge case where running template fails IF the metric is a user defined function.

-Added parameters to run_umap "wrapper" that reach more capabilities within the UMAP.m core; search "v2.1.2" in run_umap.m to see these additions.
-Fixed bugs for edge cases involving minimal data and user-defined metrics.

-Maximized UMAP parallelism speed by using all MATLAB’s assigned logical CPU cores
-Added NN-descent support for 'SEuclidean'
-New slider for shading UMAP supervisor colors
-Stochastic gradient descent halts gracefully if user closes progress window

-Stochastic gradient descent (SGD) is now parallelized by default with our MEX method. See 'sgd_tasks' in the documentation.
-'Randomize' is now true by default in order to use parallelism to accelerate both NN-descent and SGD
-Other minor bug fixes

-Improved documentation for some arguments and removed all popups when "verbose" is false
-run_umap now accepts all knnsearch arguments (except for 'SortIndices')
-Nearest neighbour computations are significantly accelerated for certain data inputs

-Removed .exe and .MEX files to comply with File Exchange requirements. Users are now encouraged to download these from our Google Drive if they wish to significantly speed up run_umap.
-Added examples 17 to 19 in run_umap header comment.

-Fixed a bug in SGD in Java where data was unintentionally stored as two distinct objects
-Added QF trees and dissimilarity plots
-Added an experimental joined_transform method that outperforms transform() when training data is missing populations

-Fixed some minor cosmetic issues such as suboptimal plot scaling

-If applying a UMAP template on data that appears to have new populations, a warning occurs and the option is given to perform a re-supervised reduction
-Fixed an indexing error occurring in smooth_knn_dist.m if data had too many identical points

-Fixed a GUI bug that would occur for users with MATLAB R2018b or earlier

-Data can now be reduced to any number of dimensions by changing the 'n_components' parameter; if reducing to more than 2 dimensions, a 3D plot is shown
-DBSCAN can be used to cluster UMAP output
-The 'n_epochs' parameter can now be manually changed

-Added precomputed parameter values for users without the Curve Fitting Toolbox
-Fixed an issue when using transform() on new data sets of same size of previous embedding and improved adjacency matrix for transform()
-Improved progress bars

-Added 2 examples (run_umap.m) showing how to perform supervised dimension reduction with UMAP
-Improved labelling of plots; for supervised UMAP, the plot includes a legend with labels from the categorical data
-Explained proper MATLAB path settings